Deploy code agent from GitHub Actions

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +1,13 @@
 ---
-title: Stitch Qa Code Agent
-emoji: 🌍
-colorFrom: green
-colorTo: red
+title: Stitch QA Code Agent
+emoji: 🧠
+colorFrom: purple
+colorTo: blue
 sdk: docker
-pinned: false
+app_port: 7860
 license: mit
-short_description: Generates code-level repair suggestions and patch guidance
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Stitch QA Code Agent
+
+Generates code-level repair suggestions and patch guidance for Stitch QA using a code-focused model.

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import os
+import torch
+import re
+
+HF_MODEL = os.getenv("HF_MODEL", "Qwen/Qwen2.5-0.5B-Instruct")
+
+tokenizer = None
+model = None
+
+app = FastAPI(title="Stitch QA Code Agent")
+
+
+class CodeRepairRequest(BaseModel):
+    project_type: str
+    file_path: str | None = None
+    code_snippet: str | None = None
+    error_log: str | None = None
+    root_cause: str | None = None
+    repair_summary: str | None = None
+
+
+@app.get("/")
+def health_check():
+    return {
+        "service": "stitch-qa-code-agent",
+        "status": "running",
+        "llm_enabled": True,
+        "llm_mode": "local-transformers",
+        "model": HF_MODEL
+    }
+
+
+def load_model():
+    global tokenizer, model
+
+    if tokenizer is None or model is None:
+        tokenizer = AutoTokenizer.from_pretrained(HF_MODEL)
+        model = AutoModelForCausalLM.from_pretrained(
+            HF_MODEL,
+            torch_dtype=torch.float32,
+            low_cpu_mem_usage=True
+        )
+
+    return tokenizer, model
+
+
+def build_prompt(request: CodeRepairRequest):
+    code = request.code_snippet or "No code snippet provided."
+    error = request.error_log or "No error log provided."
+    root_cause = request.root_cause or "No root cause provided."
+    repair_summary = request.repair_summary or "No repair summary provided."
+    file_path = request.file_path or "Unknown file"
+
+    return f"""
+Analyze the following code repair context and provide safe code-level guidance.
+
+Project type:
+{request.project_type}
+
+File path:
+{file_path}
+
+Root cause:
+{root_cause}
+
+Repair summary:
+{repair_summary}
+
+Error log:
+{error}
+
+Code snippet:
+{code}
+
+Return only these sections:
+1. Problem
+2. Safe fix approach
+3. Suggested code change
+4. Verification step
+
+Do not include system/user/assistant labels.
+Do not repeat the prompt.
+Do not invent files that are not shown.
+Do not apply changes automatically.
+Keep the answer concise.
+"""
+
+
+def call_llm(prompt: str):
+    active_tokenizer, active_model = load_model()
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a careful code repair assistant. Return only the final repair guidance."
+        },
+        {
+            "role": "user",
+            "content": prompt
+        }
+    ]
+
+    if hasattr(active_tokenizer, "apply_chat_template"):
+        formatted_prompt = active_tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+    else:
+        formatted_prompt = prompt
+
+    inputs = active_tokenizer(
+        formatted_prompt,
+        return_tensors="pt",
+        truncation=True,
+        max_length=1024
+    )
+
+    outputs = active_model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=False,
+        pad_token_id=active_tokenizer.eos_token_id
+    )
+
+    generated_text = active_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return generated_text.strip()
+
+
+def fallback_code_guidance(request: CodeRepairRequest):
+    if request.error_log:
+        summary = (
+            "A code-level issue may exist based on the provided error log. "
+            "Review the affected file, identify the failing line, apply the smallest safe change, "
+            "and rerun the project tests."
+        )
+    else:
+        summary = (
+            "No specific error log was provided. Review the code snippet manually and run the project tests "
+            "after applying any change."
+        )
+
+    return {
+        "agent": "code-agent",
+        "mode": "fallback",
+        "summary": summary,
+        "risk_level": "MEDIUM",
+        "auto_apply": False,
+        "suggested_patch": None,
+        "verification": "Rerun Stitch QA after applying any manual code changes."
+    }
+
+
+def remove_prompt_leak(text: str):
+    cleaned = text.strip()
+
+    marker_patterns = [
+        r"assistant\s*###",
+        r"assistant\s*1\.",
+        r"assistant\s*Problem",
+        r"###\s*1\.\s*Problem",
+        r"1\.\s*Problem"
+    ]
+
+    for pattern in marker_patterns:
+        match = re.search(pattern, cleaned, flags=re.IGNORECASE | re.DOTALL)
+        if match:
+            cleaned = cleaned[match.start():]
+            break
+
+    cleaned = re.sub(r"^\s*assistant\s*", "", cleaned, flags=re.IGNORECASE)
+    cleaned = re.sub(r"^\s*system\s+.*?\s+user\s+", "", cleaned, flags=re.IGNORECASE | re.DOTALL)
+
+    bad_prefixes = [
+        "system You are",
+        "user You are",
+        "Analyze the following code repair context"
+    ]
+
+    for prefix in bad_prefixes:
+        index = cleaned.lower().find(prefix.lower())
+        if index == 0:
+            return None
+
+    return cleaned.strip()
+
+
+def clean_output(text: str):
+    cleaned = remove_prompt_leak(text)
+    if not cleaned:
+        return None
+
+    cleaned = re.sub(r"\n{3,}", "\n\n", cleaned)
+    cleaned = cleaned.strip()
+
+    if not cleaned:
+        return None
+
+    if len(cleaned) < 30:
+        return None
+
+    if "system You are" in cleaned or "user You are" in cleaned:
+        return None
+
+    return cleaned
+
+
+@app.post("/suggest-code-fix")
+def suggest_code_fix(request: CodeRepairRequest):
+    fallback_result = fallback_code_guidance(request)
+
+    try:
+        prompt = build_prompt(request)
+        llm_text = call_llm(prompt)
+        cleaned_text = clean_output(llm_text)
+
+        if not cleaned_text:
+            return fallback_result
+
+        return {
+            "agent": "code-agent",
+            "mode": "llm",
+            "summary": cleaned_text,
+            "risk_level": "MEDIUM",
+            "auto_apply": False,
+            "suggested_patch": None,
+            "verification": "Apply the suggested change manually, then rerun Stitch QA verification."
+        }
+
+    except Exception as error:
+        fallback_result["llm_error"] = repr(error)
+        return fallback_result

requirements.txt

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+fastapi
+uvicorn
+pydantic
+transformers
+torch
+sentencepiece
+accelerate